A SUDAS is a system comprising at least one relay. In conventional amplify and forward (AF) relay networks, the relayed signal (transmitted from the relay node) is normally located in the same carrier frequency band as the original signal (transmitted from the source node). Orthogonal channels are assumed for relays, where time division multiplexing (TDM), frequency division multiplexing (FDM) or a code division multiplexing (CDM) is assumed. Pilot data (also called training data or reference data) provided within the payload data is commonly sufficient for synchronization and general estimation in the used carrier frequency band and related subbands. Due to consideration of the same frequency band for transmission, the pilot structure (transmitted by the source node) can be optimized for AF relay systems w.r.t. the worst case channel conditions and the applied channel estimation techniques at the destination node, cf. [9], [10]. This does not hold for a significant frequency conversion during signal transmission and forwarding.
For example, a pilot matrix design is proposed in [8], where the cascaded channel from source node to relay node and relay node to destination node is identified for an AF relay system. However, the same slowly time-variant characteristics of both channels are assumed due to transmission in the same frequency band. This is why a set of different pilot matrices (=unitary subcarrier permutation matrices applied at the relay node) can be applied assuming both channels to be quasi constant. The destination node exploits the knowledge of this set to estimate both channels.
Given a signal structure, where pilot data fields are included, methods to estimate the transmission channel or (carrier) frequency offset from these pilot fields are widely known and applied, cf. [3] and [6]. These estimates hold for the time-frequency-code-space (t-f-c-s) resource, where the pilot fields are located. Furthermore, the fact of having reciprocity of the wireless channel is widely known and often exploited, e.g. in [7], where perfect channel state information is assumed.
In [11], synchronization and channel estimation schemes in OFDM/OFDMA relay systems are considered, where difference is made between transparent and cooperative relay systems supporting an OFDM-based mobile network system. A transparent relay means that the user equipment cannot determine whether the user equipment received the signal from the base station or from the relay. However, cooperative relays interact with base station and user equipment, where special emphasis is given here to the space-time (block) coding and space-frequency (block) coding.
In [11], the pilot data within the relay payload signal can directly be used for channel estimation and synchronization. Furthermore, [11] uses a propagation delay estimation between different transmission links, e.g. between direct link and two relay links as well a compensation of the different delays for a more accurate channel estimation. Although not stated in [11], this works only as long as the delays are within the cyclic prefix of the OFDM symbol in order to avoid inter-symbol interference (ISI) and inter-carrier interference (ICI).
[11] further uses stored carrier offsets and timing offsets from earlier estimation, wherefore an identification of the transmitter device is proposed for correct table-look-up and offset compensation. This table may be kept updated. This is very essential for the cooperative system in [11], because all devices (base station, relays, user equipments) share the same t-f-c-s resources.
In [12], a special channel estimation method is disclosed, called compressed sensing, for a two-way relay network. Based on a very specific training sequence, a Gaussian random training sequence, which is transmitted by each user terminal, iterative channel estimation is done. Thus, this method performs well only in connection with applying the Gaussian random training sequence.
[13] shows an exchange of channel estimation errors in the MIMO two-way relay system using an iterative algorithm, wherein the further delay is produced by exchanging the channel estimation errors.
In [14], a MIMO processing relay node is considered, i.e. with multiple input and output antennas, while the source and destination nodes only have a single antenna. In this one-way relay system, source nodes and the relay nodes transmit training sequences to the relay node and destination node (receives training sequence from source node and relay), which is a straight forward way to measure all present links. The relay node does channel estimation as well as calculation of the relay MIMO signal processing matrix and the receiving matrix for the destination nodes by means of an iterative algorithm. The approach of [14] cannot solve the stated problem of a rapidly time-varying channel due to the heavy delay introduced by convergence time of a derivative algorithm and due to degradations of very likely outdated feedback of channel estimates from the destination nodes.
All of the above mentioned concepts have in common that effects like Doppler shift or other channel distortions are magnified due to using relay stations and are assumed to show similar characteristics. This is because a significant frequency conversion, which changes the characteristics, is not considered in these concepts.
Therefore, there is the need for an improved approach.